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Aggregated Relative Similarity (ARS): a novel similarity measure for improved personalised learning recommendation using hybrid filtering approach

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Multimedia Tools and Applications 2025年第18期84卷 18733-18780页

作者： Pal, Saurabh Dutta Pramanik, Pijush Kanti Choudhury, Prasenjit Department of Computer Science and Engineering National Institute of Technology Durgapur West Bengal Durgapur India School of Computer Applications and Technology Galgotias University Uttar Pradesh Greater Noida India Department of Computer Science and Engineering School of Engineering Brainware University Kolkata India

To improve the effectiveness of online learning, the learning materials recommendation is required to be personalised to the learner material recommendations must be personalized to learners. The existing approaches are ineffective in recommending learning materials according to the learner’s learning situation, the learner’s internal learning characteristics, and fitting to the learner’s preferences and learning suitability. In this work, we propose a hybrid recommendation approach cthat combines the advantages of knowledge-based and collaborative filtering approaches. The knowledge-based filtering approach enables us to find the most suitable learning materials that fit the learner’s situation and learning state by appropriately mapping the learners’ contexts with the metadata of the learning materials. We use collaborative filtering to refine learning material selection by considering learner-learner similarity. We propose a novel similarity measure, aggregated relative similarity (ARS), to determine similar learners based on implicit learner characteristics such as education, knowledge, cognitive ability, and learning style. The experimental results show that the proposed ARS algorithm outperforms popular similarity measures such as Jaccard similarity, Sorensen-Dice coefficient, and overlap coefficient. The overall recommendation performance of the proposed hybrid approach attained mean absolute error (MAE) and root mean square error (RMSE) of 0.681 and 0.9198, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Collaborative filtering

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Deep Learning Based Face Detection and Identification of Criminal Suspects

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computers, Materials & Continua 2023年第2期74卷 2331-2343页

作者： S.Sandhya A.Balasundaram Ayesha Shaik School of Computer Science and Engineering Vellore Institute of Technology(VIT)Chennai600127India School of Computer Science and Engineering Center for Cyber Physical SystemsVellore Institute of Technology(VIT)Chennai600127India

Occurrence of crimes has been on the constant rise despite the emerging discoveries and advancements in the technological field in the past *** of the most tedious tasks is to track a suspect once a crime is *** most of the crimes are committed by individuals who have a history of felonies,it is essential for a monitoring system that does not just detect the person’s face who has committed the crime,but also their ***,a smart criminal detection and identification system that makes use of the OpenCV Deep Neural Network(DNN)model which employs a Single Shot Multibox Detector for detection of face and an auto-encoder model in which the encoder part is used for matching the captured facial images with the criminals has been *** detection and extraction of the face in the image by face cropping,the captured face is then compared with the images in the *** comparison is performed by calculating the similarity value between each pair of images that are obtained by using the Cosine Similarity *** plotting the values in a graph to find the threshold value,we conclude that the confidence rate of the encoder model is 0.75 and above.

关键词： Deep learning opencv deep neural network single shot multi-box detector auto-encoder cosine similarity

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2812 | An Interaction-Aware Two-Level Robotic Planning and Control System for Vegetation Override 21

2812 | An Interaction-Aware Two-Level Robotic Planning and C...

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21st International and 12th Asia-Pacific Regional Conference of the International Society for Terrain-Vehicle Systems, ISTVS 2024

作者： Noren, Charles Shirose, Burhanuddin Vundurthy, Bhaskar Scherer, Sebastian Travers, Matthew The Robotics Institute School of Computer Science Carnegie Mellon University PA United States

ISBN: (纸本)9781942112570

During off-road operations, mobile robotic platforms often encounter objects that influence the platform's route. As determining the outcome of an interaction with an object (e.g., overriding) is difficult, many robotic planners are designed to avoid interactions with all environmental objects. Yet, this object-adverse planning behavior is not reflected in the actions of expert human operators, who may interact with objects in order to find a viable path towards their goal. This work intends to emulate that human operator intuition. Our objective is to improve the performance of robotic traversals in off-road terrains through the development of a planning paradigm that allows certain safe contact with environmental objects. Specifically, we design a two-level hierarchical planning and control system which couples a contact-informed regional motion planner with contact-constrained local nonlinear trajectory optimization techniques. The approach is demonstrated for classes of vegetative objects which are characterized by existing parameterized collision models from the terramechanics community. The top level of the hierarchy combines sampling-based planning techniques with a set of override (velocity) constraints derived from these collision models during the search for a minimum-time trajectory towards the platform's goal. This minimum-time trajectory is then passed to the lower-level of the architecture, which utilizes direct collocation and model predictive control techniques to ensure that the velocity constraints are enforced during trajectory execution. The capabilities of the architecture are shown both in simulation and onboard a robotic platform, where vegetation is reasoned about and then overridden depending on the environment, platform, and object geometry. © 2024 International Society for Terrain-Vehicle Systems. All rights reserved.

关键词： Off road vehicles

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Optimization enabled LeNet for big data classification using MapReduce framework on COVID-19 data

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Australian Journal of Electrical and Electronics engineering 2024年第4期21卷 409-424页

作者： Patle, Bhagyashree R. Vijayarajan, V. VIT Vellore India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

The big data clustering is a requisite for generating the data in the digitalised globe. The old-fashioned clustering approaches are not large sized and highly unorganised big data. Thus, to obtain the efficiency of big data clustering, a new architecture is required. This work introduces the Jaya African Vulture Optimization Algorithm-based LeNet (JAVO-based LeNet) for big data classification on COVID-19. The big data clustering is detected by using the LeNet model, which is tuned by the JAVO algorithm. Here, the JAVO algorithm is the integration of the Jaya algorithm and AVOA algorithm. Further, using the Deep Embedded Clustering (DEC) technique for partitioning the smaller data to improve the performance of big data classification. The classification process for COVID-19 is established in the MapReduce (MR) framework. For each mapper, the pre-processing is completed based on Min-Max normalisation and the feature fusion is performed based on the Hellinger distance, which is measured with a deep residual network (DRN). Finally, the classification process for COVID-19 is done by considering the fused data acquired from each mapper with LeNet, which is tuned by the JAVO algorithm. Furthermore, the analysis of the proposed method attains the testing accuracy of 0.948, specificity of 0.939 and sensitivity of 0.950. ©, Engineers Australia.

关键词： Cluster analysis

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Sentiment analysis of multi social media using machine and deep learning models: a review

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Multimedia Tools and Applications 2024年第42期83卷 90033-90051页

作者： Vasanthi, P. Madhu Viswanatham, V. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India

The Internet has become an important origin of text information, which is then used inside a wide range of research domains. This has been regarded as a necessary foundation for institutions to obtain valuable information in addition to assessing consumers' feelings and views on a particular subject. Message clustering is the method of assigning specific tags to known categories based on one ‘s content material. It is difficult to classify the text using aspect-based sentiment. So each research on sentiment has addressed this issue, as existing research focuses on file and predominant component assessment instead of the specifics of the emotions. This survey reviewed the sentiment analysis based on both X and Facebook. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Sentiment analysis

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HSALC: hard sample aware label correction for medical image classification

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Multimedia Tools and Applications 2024年 1-21页

作者： Wang, Yangtao Ye, Yicheng Xie, Yanzhao Tang, Maobin Fan, Lisheng Metaverse Research Institute School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China

Medical image automatic classification has always been a research hotspot, but the existing methods suffer from the label noise problem, which either discards those samples with noisy labels or produces wrong label correction, seriously preventing the medical image classification performance improvement. To address the above problems, in this paper, we propose a hard sample aware label correction (termed as HSALC) method for medical image classification. Our HSALC mainly consists of a sample division module, a clean·hard·noisy (termed as CHN) detection module and a label noise correction module. First, in the sample division module, we design a sample division criterion based on the training difficulty and training losses to divide all samples into three preliminary subsets: clean samples, hard samples and noisy samples. Second, in the CHN detection module, we add noise to the above clean samples and repeatedly adopt the sample division criterion to effectively detect all data, which helps obtain highly reliable clean samples, hard samples and noisy samples. Finally, in the label noise correction module, in order to make full use of each available sample, we train a correction model to purify and correct the wrong labels of noisy samples as much as possible, which brings a highly purified dataset. We conduct extensive experiments on five image datasets including three medical image datasets and two natural image datasets. Experimental results demonstrate that HSALC can greatly promote classification performance on noisily labeled datasets, especially with high noise ratios. The source code of this paper is publicly available at GitHub: https://***/YYC117/HSALC. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image classification

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Automatic Sentimental Analysis by Firefly with Levy and Multilayer Perceptron

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computer Systems science & engineering 2023年第9期46卷 2797-2808页

作者： D.Elangovan V.Subedha School of Computing Computer Science and EngineeringSathyabama Institute of Science and TechnologyChennai600118India Computer Science and Engineering Panimalar Institute of TechnologyChennai600069India

The field of sentiment analysis(SA)has grown in tandem with the aid of social networking platforms to exchange opinions and *** people share their views and ideas around the world through social media like Facebook and *** goal of opinion mining,commonly referred to as sentiment analysis,is to categorise and forecast a target’s *** on if they provide a positive or negative perspective on a given topic,text documents or sentences can be *** compared to sentiment analysis,text categorization may appear to be a simple process,but number of challenges have prompted numerous studies in this area.A feature selection-based classification algorithm in conjunction with the firefly with levy and multilayer perceptron(MLP)techniques has been proposed as a way to automate sentiment analysis(SA).In this study,online product reviews can be enhanced by integrating classification and feature *** firefly(FF)algorithm was used to extract features from online product reviews,and a multi-layer perceptron was used to classify sentiment(MLP).The experiment employs two datasets,and the results are assessed using a variety of *** account of these tests,it is possible to conclude that the FFL-MLP algorithm has the better classification performance for Canon(98%accuracy)and iPod(99%accuracy).

关键词： Firefly algorithm feature selection feature extraction multi-layer perceptron automatic sentiment analysis

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MESSI: Task Mapping and Scheduling Strategy for FPGA-based Heterogeneous Real-Time Systems

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ACM Transactions on Design Automation of Electronic Systems 2025年第3期30卷 1-29页

作者： Ahmadi-Pour, Sallar Saha, Sangeet Mcdonald-Maier, Klaus Drechsler, Rolf Institute of Computer Science University of Bremen Faculty 3 Mathematics and Computer Science Bremen Bremen Germany Robotics and Embedded Systems Research Group University of Essex School of Computer Science and Electronic Engineering Colchester United Kingdom Cyber-Physical Systems DFKI Rheinland-Pfalz Kaiserslautern Germany

Continuous demands for improved performance within constrained resource budgets are driving a move from homogeneous to heterogeneous processing platforms for the implementation of today's Real-Time (RT) embedded systems. The applications executing on such systems are typically represented as a Precedence Task Graph (PTG), where a node represents a task or algorithm for one functionality and edges represent the complex interactions between multiple functionalities. Due to RT constraints, the task graph needs to be executed within a specified deadline. Although some existing studies have looked into solving this challenge, comprehensive studies that combine the theoretical features of RT task-graph mapping and scheduling with practical runtime architectural characteristics have mostly been ignored to date. Hence, in this article, we consider the challenge of scheduling an RT application modeled as a single PTG, with the objective of minimizing the overall execution time under Hardware (HW) resource and deadline constraints for heterogeneous Central Processing Unit (CPU) + Field Programmable Gate Array (FPGA) architectures. First, we introduce an optimal solution using Integer Linear Programming (ILP). However, this ILP-based optimal solution suffers from computational complexity and does not scale well even for moderately large problem sizes. Hence, we additionally propose heuristic algorithms for task mapping and scheduling. The efficiency of the proposed scheme, named MESSI, has been evaluated through experiments using PTG on a practical CPU+FPGA system regarding current technology restrictions. Our experiments demonstrate that performance gains of 55.6% and area usage reductions of 46.3% are possible compared to full Software (SW) and HW execution, respectively. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Integer programming

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Enhancing Data Forwarding Efficiency in SIoT with Multidimensional Social Relations

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computers, Materials & Continua 2024年第1期78卷 1095-1113页

作者： Fang Xu Songhao Jiang Yi Ma Manzoor Ahmed Zenggang Xiong Yuanlin Lyu School of Computer and Information Science Hubei Engineering UniversityXiaogan432000China School of Computer Science and Information Engineering Hubei UniversityWuhan430062China Artificial Intelligence Industrial Technology Research Institute Hubei Engineering UniversityXiaogan432000China

Effective data communication is a crucial aspect of the Social Internet of Things(SIoT)and continues to be a significant research *** paper proposes a data forwarding algorithm based on Multidimensional Social Relations(MSRR)in SIoT to solve this *** proposed algorithm separates message forwarding into intra-and cross-community forwarding by analyzing interest traits and social connections among *** new metrics are defined:the intensity of node social relationships,node activity,and community *** the community,messages are sent by determining which node is most similar to the sender by weighing the strength of social connections and node *** a node performs cross-community forwarding,the message is forwarded to the most reasonable relay community by measuring the node activity and the connection between *** proposed algorithm was compared to three existing routing algorithms in simulation *** indicate that the proposed algorithmsubstantially improves message delivery efficiency while lessening network overhead and enhancing connectivity and coordination in the SIoT context.

关键词： SIoT data forwarding social attributes social relations community

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Secure Virtual Machine Live Migration using Advanced Metric Encryption

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Recent Advances in computer science and Communications 2023年第6期16卷 16-21页

作者： Narayanan, Gokul Geetha Saravanaguru, Ramaiah Kannan School of Computer Science and engineering Vellore Institute of Technology Vellore India

Cloud is based on the underlying technology of virtualization. Here, the physical servers are divided into multiple virtual servers. Through the technology of virtualization, each virtual server contains virtual machines. Live virtual machine migration is to have migration time and inactive time with minimal duration. Various machine-learning approaches have been investigated and research gaps were identified to enhance the security features during the migration process. Moreover, a secure virtual machine live migration is proposed using Advanced Metric Encryption (AME). Considering the duration of live migration in data centers as well as ensuring the security aspects, the proposed model has been tested and evaluated. © 2023 Bentham science Publishers.

关键词： Virtual machine

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